Assessment of neurovascular dynamics during transient ischemic attack by the novel integration of micro-electrocorticography electrode array with functional photoacoustic microscopy.

This study developed a novel system combining a 16-channel micro-electrocorticography (µECoG) electrode array and functional photoacoustic microscopy (fPAM) to examine changes in neurovascular functions following transient ischemic attack (TIA) in rats. To mimic the pathophysiology of TIA, a modified photothrombotic ischemic model was developed by using 3 min illumination of 5 mW continuous-wave (CW) green laser light focusing on a distal branch of the middle cerebral artery (MCA). Cerebral blood volume (CBV), hemoglobin oxygen saturation (SO2), somatosensory evoked potentials (SSEPs) and alpha-to-delta ratio (ADR) were measured pre- and post-ischemia over a focal cortical region (i.e., 1.5×1.5 mm(2)). Unexpectedly, the SO2, peak-to-peak amplitude (PPA) of SSEPs and ADR recovered and achieved levels greater than the baseline values at the 4th hour post-ischemia induction without any intervention, whereas the CBV value only partially recovered. In other words, transient ischemia led to increased neural activity when the relative CBV was reduced, which may further compromise neural integrity or lead to subsequent vascular disease. This novel µECoG-fPAM system complements currently available imaging techniques and represents a promising technology for studying neurovascular coupling in animal models.

Prospective assessment and validation of rhythmic and periodic pattern detection in NeuroTrend: A new approach for screening continuous EEG in the intensive care unit.

BACKGROUND: NeuroTrend is a computational method that analyzes long-term scalp EEGs in the ICU according to ACNS standardized critical care EEG terminology (CCET) including electrographic seizures. At present, it attempts to become a screening aid for continuous EEG (cEEG) recordings in the ICU to facilitate the review process and optimize resources. METHODS: A prospective multicenter study was performed in two neurological ICUs including 68 patients who were subjected to video-cEEG. Two reviewers independently annotated the first minute of each hour in the cEEG according to CCET. These segments were also screened for faster patterns with frequencies higher than 4 Hz. The matching annotations (2911 segments) were then used as gold standard condition to test sensitivity and specificity of the rhythmic and periodic pattern detection of NeuroTrend. RESULTS: Interrater agreement showed substantial agreement for localization (main term 1) and pattern type (main term 2) of the CCET. The overall detection sensitivity of NeuroTrend was 94% with high detection rates for periodic discharges (PD = 80%) and rhythmic delta activity (RDA = 82%). Overall specificity was moderate (67%) mainly because of false positive detections of RDA in cases of general slowing. In contrast, a detection specificity of 88% for PDs was reached. Localization revealed only a slight agreement between reviewers and NeuroTrend. CONCLUSIONS: NeuroTrend might be a suitable screening tool for cEEG in the ICU and has the potential to raise efficiency of long-term EEG monitoring in the ICU. At this stage, pattern localization and differentiation between RDA and general slowing need improvement. This article is part of a Special Issue entitled "Status Epilepticus".

[Usefulness of electroencephalograms in evaluating transient global amnesia]. / Utilidad de la electroencefalografia en la valoracion de la amnesia global transitoria.

INTRODUCTION: Transient global amnesia (TGA) is a perfectly well defined clinical picture, but nevertheless even today its aetiology remains unknown. The three most widely accepted theories suggest it has a vascular origin, it is related with the pathophysiology of migraine or it is of an epileptiform nature. AIM: To analyse whether there is an electroencephalographic pattern that is consistently repeated in a series of electro-encephalograms (EEG) carried out on patients with TGA. PATIENTS AND METHODS: The study consists in a retrospective analysis of a sample of 345 patients referred to have an EEG after an episode of TGA. RESULTS: In almost 20% of the EEGs something that could be considered abnormal was found, although most of these findings (64%) were of little pathological significance. Of the remaining 26%, attention should be drawn to the cases of two patients with subclinical rhythmic electroencephalogram discharges of adults (a pattern with a meaning that is not altogether clear and which has previously been associated with TGA). CONCLUSIONS: A considerable percentage of patients have TGA and EEG alterations, although most of them are of scarce pathological significance or can be attributed to some other underlying condition. We have not succeeded in identifying any pattern that is consistently repeated. Our results suggest that the EEG is a test with low diagnostic effectiveness in this pathology and it is necessary to reconsider the need to systematically perform such tests in suspected cases of TGA.

Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold.

It takes effort and time to tame one's impulses. Although medial prefrontal cortex (mPFC) is broadly implicated in effortful control over behavior, the subthalamic nucleus (STN) is specifically thought to contribute by acting as a brake on cortico-striatal function during decision conflict, buying time until the right decision can be made. Using the drift diffusion model of decision making, we found that trial-to-trial increases in mPFC activity (EEG theta power, 4-8 Hz) were related to an increased threshold for evidence accumulation (decision threshold) as a function of conflict. Deep brain stimulation of the STN in individuals with Parkinson's disease reversed this relationship, resulting in impulsive choice. In addition, intracranial recordings of the STN area revealed increased activity (2.5-5 Hz) during these same high-conflict decisions. Activity in these slow frequency bands may reflect a neural substrate for cortico-basal ganglia communication regulating decision processes.

Identifying robust and sensitive frequency bands for interrogating neural oscillations.

Recent years have seen an explosion of interest in using neural oscillations to characterize the mechanisms supporting cognition and emotion. Oftentimes, oscillatory activity is indexed by mean power density in predefined frequency bands. Some investigators use broad bands originally defined by prominent surface features of the spectrum. Others rely on narrower bands originally defined by spectral factor analysis (SFA). Presently, the robustness and sensitivity of these competing band definitions remains unclear. Here, a Monte Carlo-based SFA strategy was used to decompose the tonic ("resting" or "spontaneous") electroencephalogram (EEG) into five bands: delta (1-5Hz), alpha-low (6-9Hz), alpha-high (10-11Hz), beta (12-19Hz), and gamma (>21Hz). This pattern was consistent across SFA methods, artifact correction/rejection procedures, scalp regions, and samples. Subsequent analyses revealed that SFA failed to deliver enhanced sensitivity; narrow alpha sub-bands proved no more sensitive than the classical broadband to individual differences in temperament or mean differences in task-induced activation. Other analyses suggested that residual ocular and muscular artifact was the dominant source of activity during quiescence in the delta and gamma bands. This was observed following threshold-based artifact rejection or independent component analysis (ICA)-based artifact correction, indicating that such procedures do not necessarily confer adequate protection. Collectively, these findings highlight the limitations of several commonly used EEG procedures and underscore the necessity of routinely performing exploratory data analyses, particularly data visualization, prior to hypothesis testing. They also suggest the potential benefits of using techniques other than SFA for interrogating high-dimensional EEG datasets in the frequency or time-frequency (event-related spectral perturbation, event-related synchronization/desynchronization) domains.

Assessment of CNS effects of antiepileptic drugs by using quantitative EEG measures.

PURPOSE: Antiepileptic drugs (AEDs) can be associated with adverse neurologic effects including cognitive dysfunction. Objective methods for recognizing AED effects on the brain could be valuable for long-term management. We compared quantitative EEG measures and cognitive tests in a group of patients beginning or ending AED therapy. METHODS: Subjects included 20 patients beginning AED therapy (AEDon), 12 patients stopping AED therapy (AEDoff), 33 patient controls receiving stable AED therapy (AEDco), and 73 healthy controls (Nco). All subjects underwent structured EEG recording and a cognitive test battery before change in AED dose and again 12-16 weeks later, >or=4 weeks after the last dose change. Four occipital EEG measures (peak frequency, median frequency, relative theta and delta power) were analyzed. Cognitive test changes were scored by using test-retest regression equations based on the Nco subjects. Wilcoxon tests were used for two-group comparisons. RESULTS: AEDons had a significant decrease, and AEDoffs, a significant increase in the peak frequency of the EEG rhythm, as compared with controls. Results for median frequency and theta power were similar. Change in the EEG peak frequency correlated with an aggregate cognitive change measure (r2= 0.71; p < 0.001), individual cognitive measures, and subjective complaints. Of the combined AEDon/AEDoff patients, 58% exceeded the 95% confidence interval for test-retest change in EEG peak frequency. CONCLUSIONS: Quantitative measures derived from the occipital EEG are sensitive to AEDs and correlate with AED-related cognitive effects and subjective complaints. Although this correlation does not indicate a direct relation, quantified EEG may be a practical measure of AED impact on the brain.

Neuroprotection assessment by topographic electroencephalographic analysis: effects of a sodium channel blocker to reduce polymorphic delta activity following ischaemic brain injury in rats.

The spatiotemporal electroencephalogram (EEG) pathology associated with brain injury was studied using high-resolution, 10-electrode cortical EEG mapping in a rat model of middle cerebral artery occlusion (MCAo). Using this model we evaluated the ability of the novel sodium channel blocker and neuroprotective agent RS100642 to resolve injury-induced EEG abnormalities as a measure of neurophysiological recovery from brain injury. The middle cerebral artery (MCA) was occluded for 1 h during which a dramatic loss of EEG power was measured over the injured cortex with near complete recovery upon reperfusion of blood to the MCA region in all rats. The resultant progression of the MCAo/reperfusion injury (6-72 h) included the appearance of diffuse polymorphic delta activity (PDA), as visually indicated by the presence of high-amplitude slow-waves recorded from both brain hemispheres. PDA was associated with large increases in EEG power, particularly evident in outer 'peri-infarct' regions of the ipsilateral parietal cortex as visualized using topographic EEG mapping. Post-injury treatment with RS100642 (1.0 mg/kg, i.v.) significantly reduced the PDA activity and attenuated the increase in EEG power throughout the course of the injury. These effects were associated with a reduction in brain infarct volume and improved neurological function. These methods of EEG analysis may be helpful tools to evaluate the physiological recovery of the brain from injury in humans following treatment with an experimental neuroprotective compound.

The future of computer-assisted investigation of the polysomnogram: sleep microstructure.

Previous attempts at automated analysis of sleep were mainly directed towards imitating the Rechtschaffen and Kales rules (RKR) in order to save scoring time and further objectify the procedure. RKR, however, do not take into consideration the sleep microstructure of REM, stage 2, and SWS. While the microstructure of stage 2 has been analyzed in the past decade, the microstructure of REM and SWS are virtually unknown. In stage 2 the amount and distribution of spindles, K complexes, and arousal reactions have been studied. At least two types of spindles (12/s and 14/s) with different dynamics and locations have been identified. Two different shapes for K complexes have been described: one related to external sensory stimuli with similarities to evoked potentials and another one more related to sinusoidal slow wave activity seen in SWS. These two different K complex shapes have different distributions and, obviously, different functions. The authors also suggest that one should differentiate between arousal reactions and true arousals. Recent investigations suggest two types of delta waves in SWS. The more sinusoidal 1-3/s delta waves with a frontal maximum are already seen with lower amplitude in late stage 2 and increase their amplitude and incidence towards stage 3 and Stage 4. The other delta-wave type is slower (< 1/s), polymorphic, and has varying amounts of theta and higher frequency waves superimposed. During REM sleep it seems to be important to separate phases with rapid eye movements from those with none (REM sine REM), and count the amount and distribution of sawtooth activity. Background activity during REM and REM sine REM, as well as intra- and interhemispheric coherence should be analyzed separately. Only if the microstructure of the sleep EEG can be analyzed automatically using newer techniques such as transformation into wavelets and pattern classification with neuronal networks, and only if we learn more about the importance of microstructure elements, can automated sleep analysis go beyond the limited information obtained from scoring according to RKR.

Bedside clinical and electrophysiological assessment: assessment of change in vulnerable patients.

Current approaches to the diagnosis of delirium are based upon the recognition of symptoms that emerge in the pathological state. As an alternative, we propose an approach to case identification for research purposes based on the recognition of significant changes in the cognitive or cerebral state of the individual patient. Categorical change can be defined using prediction intervals calculated from repeated measures on a population of medically stable subjects. Data from subjects enrolled in a prospective study of delirium in a long-term care population were utilized to calculate prediction intervals for the Mini-Mental Status Examination and for measures of the electroencephalographic background frequency as obtained with a two-channel microprocessor-based EEG device. Preliminary findings support the validity of this quantitative approach for defining changes in brain state. Future research should evaluate both cognitive and electrophysiological techniques for monitoring vulnerable patients.